Food security will become increasingly challenged over the coming decades, and sustainable intensification is often touted as an ideal way to increase yields while limiting negative environmental impacts. Yet, the extent to which sustainable intensification can increase yields remains unclear. We systematically reviewed the literature to assess the extent to which sustainable intensification can increase yields across South Asia, a region that is expected to face some of the greatest food security challenges over the coming decades. We found that yield gains from sustainable intensification interventions were heterogeneous, and that the average yield gain across all studi es was 21%. Residue retention and the use of organic fertilizers were, in particular, associated with significant and positive yield gains, though the use of organic fertilizers was not always profitable, likely due to large subsidies provided for mineral fertilizers across South Asia. Our work also revealed biases in the current sustainable intensification literature, with research clustered in highly productive, irrigated, and commodity cropping systems, which do not represent large portions of agricultural systems across South Asia. Our results highlight that sustainable intensification interventions should play an important role in increasing food production across South Asia, but yield gains from these interventions are modest compared to estimated yield gaps across the region.

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites.